
********************************************************************************************************************
*** Pro-equality initiatives increase expressed sexism among men but may improve trust among women football fans ***
********************************************************************************************************************

*** Authors:  
*** Victor Araújo (University of Reading) 
*** Malu A. C. Gatto (University College London)

*** Date: 02 June 2025


*** IMPORTANY NOTE: 
*** This do-file carries out the analyses conducted with data from our survey experiment (reported in Figures 3, 4, and 5 in the main manuscript). 
*** To replicate the analyses conducted with observational survey data (reported in Figure 1 in the main text), please see "AraujoGatto_Observationalanalysis.do". 



*** Installing required packages
ssc install estout, replace
ssc install blindschemes, replace
set scheme plotplain
ssc install coefplot, replace
ssc install cibar, replace
ssc install center, replace 

************************
*** Opening the data ***
************************

import delimited 
* Add your path to "AraujoGatto_Experimentaldata.csv" 


**********************************
*** Dropping invalid responses ***
**********************************


** Drop not finished as per consent form
drop if finished == 0 // dropping 635

** Drop did not consent as per consent form 
drop if q12 == 2 // dropping 59  

** Drop if under age as per ethics application  
drop if q31 == 9 // dropping 179 

** Drop if not Bahia fan as per pre-analysis plan  
drop if q22 == 2 // dropping 27 

** Note: Before cleaning process N=3,043. After cleaning process, we end up with 2,143 completed responses.



****************************
*** Generating variables ***
****************************

****** Identify condition respondents have been randomised into ****** 

* 0 = Baseline (FPF)
* 1 = Exploratory mixed-treatment (Ba-Vi)
* 2 = Main treatment (ECB)

gen treat_gen =.
replace treat_gen = 0 if treat_m3 ==1
replace treat_gen = 1 if treat_m2 ==1
replace treat_gen = 2 if treat_m1 ==1


** Binary treatment 
* 0 = Baseline 
* 1 = Main treatment 

gen treat_gen_bi =.
replace treat_gen_bi = 0 if treat_gen ==0
replace treat_gen_bi = 1 if treat_gen ==2 


****** Outcome variables ****** 

** DV: Attitudes towards sexism in football

* Question: 
*To what extent to you agree or disagree with each of the below statements (1 = strongly disagree / 7=strongly agree. Note: Higher values correspond to more sexist attitudes (i.e., agreement with sexist statements)

* Statements: 
*gen_1 (based on q96_1, q106_1, q116_1): The performance of a man player is usually better than the performance of a woman player.
*gen_2 (based on q97_1, q107_1, q117_1): A man manager would be more qualified than a woman manager to train the Brazilian soccer team. 
*gen_3 (based on q98_1, q108_1, q118_1): A man player should receive more even when he has the same performance as a woman player. 
*gen_4 (based on q99_1, q109_1, q119_1): Men players are smarter on the field than women players
*gen_5 (based on q910_1, q1010_1, q1110_1): In general, men referees make fewer mistakes than women referee.
*gen_6 (based on q911_1, q1011_1, q1111_1): Men's football is better than women's football.


sum q96_1 q106_1 q116_1 q97_1 q107_1 q117_1 q98_1 q108_1 q118_1 q99_1 q109_1 q119_1 q910_1 q1010_1 q1110_1 q911_1 q1011_1 q1111_1

gen gen_1 = .
replace gen_1 = q96_1 if treat_gen == 2
replace gen_1 = q106_1 if treat_gen == 1 
replace gen_1 = q116_1 if treat_gen == 0

gen gen_2 = .
replace gen_2 = q97_1 if treat_gen == 2
replace gen_2 = q107_1 if treat_gen == 1 
replace gen_2 = q117_1 if treat_gen == 0

gen gen_3 = .
replace gen_3 = q98_1 if treat_gen == 2
replace gen_3 = q108_1 if treat_gen == 1 
replace gen_3 = q118_1 if treat_gen == 0

gen gen_4 = .
replace gen_4 = q99_1 if treat_gen == 2
replace gen_4 = q109_1 if treat_gen == 1 
replace gen_4 = q119_1 if treat_gen == 0

gen gen_5 = .
replace gen_5 = q910_1 if treat_gen == 2
replace gen_5 = q1010_1 if treat_gen == 1 
replace gen_5 = q1110_1 if treat_gen == 0

gen gen_6 = .
replace gen_6 = q911_1 if treat_gen == 2
replace gen_6 = q1011_1 if treat_gen == 1 
replace gen_6 = q1111_1 if treat_gen == 0

gen gen_all = gen_1 + gen_2 + gen_3 + gen_4 + gen_5 + gen_6 
pwcorr gen_1 gen_2 gen_3 gen_4 gen_5 gen_6, sig


** DV: Sexist attitudes with Principal Component Aanalysis (PCA) 

pca gen_1 gen_2 gen_3 gen_4 gen_5 gen_6

screeplot, title("Sexist attitudes") xtitle("Number of factors") yline(1, lcolor(red)) ylabel(0(1)4) ci(het) legend(cols(2) position(6)) 
graph save gen_pca, replace 

predict gen_pca, score 


** DV: Support for club's social role

* Question: 
*To what extent to you agree or disagree with each of the below statements (1 = strongly disagree / 7=strongly agree. Note: Higher values correspond to more sexist attitudes (i.e., agreement with sexist statements)

* Statements: 
*role_race (based on q122_1): The ECB should promote initiatives against structural racism.
*role_inclusion (based on q123_1): The ECB should promote initiatives supporting social inclusion.
*role_gender (based on q124_1): The ECB should promote initiatives supporting gender equality.
*role_lgbt (based on q125_1): The ECB should promote initiatives against LBGTQ-phobia.
*role_harrassment (based on q126_1): The ECB should promote initiatives against sexual harassment.
*role_environment (based on q127_1): The ECB should promote initiatives supporting environmental protection.

rename q122_1 role_race
rename q123_1 role_inclusion
rename q124_1 role_gender 
rename q125_1 role_lgbt
rename q126_1 role_harrassment
rename q127_1 role_environment


** DV: Campaign evaluations 

*campaign_1 (based on q91, q101, q111): How do you evaluate this campaign promoted by the Sao Paulo Football Federation/the Esporte Clube Bahia? (in the original raw data, 5 = very bad / 1 =  excellent)
*campaign_2 (based on q92, q102, q112): Through this initiative, the Sao Paulo Football Federation/the Esporte Clube Bahia made a public commitment against gender discrimination. What do you think about the Sao Paulo Football Federation/the Esporte Clube Bahia making a public commitment to this issue? (in the original raw data, 5 = very bad / 1 =  excellent)

** q101 had an error in the survey programming, so we correct the values of labels
tab q101
recode q101 (2=1) (3=2) (4=3) (5=4) (6=5)
tab q101

gen campaign_1 = .
replace campaign_1 = q91 if treat_gen == 2
replace campaign_1 = q101 if treat_gen == 1 
replace campaign_1 = q111 if treat_gen == 0

*We invert the scale so that higher values correspond to positive evaluations
recode campaign_1 (5=1) (4=2) (3=3) (2=4) (1=5)
tab campaign_1

gen campaign_2 = .
replace campaign_2 = q92 if treat_gen == 2
replace campaign_2 = q102 if treat_gen == 1 
replace campaign_2 = q112 if treat_gen == 0

*We invert the scale so that higher values correspond to positive evaluations
recode campaign_2 (5=1) (4=2) (3=3) (2=4) (1=5) 
tab campaign_2

gen campaign_both = campaign_1 + campaign_2 
pwcorr campaign_1 campaign_2, sig


** DV: Club responsiveness
*club_1 (based on q93, q103, q113): The ECB board is interested in what fans think.
*club_2 (based on q94, q104, q114): The ECB board considers fans' opinions when planning or changing services to members and social initiatives.
*club_3 (based on q95, q105, q115): The ECB board considers fans' opinions when planning affirmative action policies. 

gen club_1 = .
replace club_1 = q93 if treat_gen == 2
replace club_1 = q103 if treat_gen == 1 
replace club_1 = q113 if treat_gen == 0
tab club_1

gen club_2 = .
replace club_2 = q94 if treat_gen == 2
replace club_2 = q104 if treat_gen == 1 
replace club_2 = q114 if treat_gen == 0
tab club_2

gen club_3 = .
replace club_3 = q95 if treat_gen == 2
replace club_3 = q105 if treat_gen == 1 
replace club_3 = q115 if treat_gen == 0
tab club_3

gen club_all = club_1 + club_2 + club_3
pwcorr club_1 club_2 club_3, sig


* DV: Club responsiveness with Principal Component Aanalysis (PCA) 

pca club_1 club_2 club_3

screeplot, title("Responsiveness to fans") xtitle("Number of factors") yline(1, lcolor(red)) ylabel(0(1)4) ci(het) legend(cols(2) position(6)) 
graph save gen_pca, replace 

predict club_pca, score 




******* Control variables *******

gen female = q41
recode female (1=1) (2=0) (3=0)
tab female 

rename q31 age

tab q42
gen white = q42 
recode white (4=1) (11 = 0) (6 =0) (7 =0) (8=0) (9=0) (10 = 0) (12 = 0) (13 = 0)
tab white 

rename q43 educ 

gen catol =. 
replace catol = 1 if q45 == 4
replace catol = 0 if catol ==.

rename q46 income 
recode income (8=.) 

rename q47 pbf
recode pbf (2=0) 

tab q49
gen marital = q49 
tab marital

gen socio = 1
replace socio = 0 if q52=="8"

tab q53
gen torcida = q53
tab torcida 

tab q54 
gen satsfaction_director = q54 

rename q57 freq_stadium
recode freq_stadium (1=5) (2=4) (3=3) (4=2) (5=1)
tab freq_stadium // note that majority of respondents are hand-core fans; go to the stadium for every game; create variable to differentiate them from others 

gen freq_stadium_bi = freq_stadium
recode freq_stadium_bi (1=0) (2=0) (3=0) (4=0) (5=1)
tab freq_stadium_bi

gen coleta_email = tipo_coleta_cod


label variable female "Woman" 
label variable age "Age" 
label variable white "White" 
label variable educ "Education" 
label variable catol "Catholic" 
label variable income "Income" 
label variable pbf "Cash transfer" 
label variable marital "Marital status"
label variable socio "Club member" 
label variable torcida "Fanclub member" 
label variable satsfaction_director "Satisfaction adm" 
label variable freq_stadium_bi "Frequency stadium" 
label variable coleta_email "Survey recruitment" 


**************************************************************
************ MAIN ANALYSES REPORTED IN MANUSCRIPT ************
**************************************************************

*** Centering outcome variables so results are shown in standard deviations 

center gen_all gen_pca role_race role_gender role_harrassment role_lgbt role_inclusion role_environment club_all club_pca campaign_both, inplace standardize 

su gen_all gen_pca role_race role_gender role_harrassment role_lgbt role_inclusion role_environment club_pca campaign_both

**************************************************************
******* Figure 3: Treatment impact on sexist attitudes *******
**************************************************************


****** PANEL A, index ****** 

reg gen_all treat_gen_bi if female==0, robust
estimates store Men


reg gen_all treat_gen_bi if female==1, robust
estimates store Women

coefplot Men Women, title("(A) DV: Sexist attitudes (index)") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons) ylabel("") xtitle("Standardised coefficients") levels(95 90) ciopts(recast(. rcap))
graph save panelA, replace

****** PANEL B, index with controls ****** 

reg gen_all treat_gen_bi age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email if female==0, robust
estimates store Men

reg gen_all treat_gen_bi age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email if female==1, robust
estimates store Women

coefplot Men Women, title("(B) DV: Sexist attitudes (index) with controls") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email) ylabel("") xtitle("Standardised coefficients") levels(95 90) ciopts(recast(. rcap))
graph save panelB, replace

****** PANEL C, pca ****** 

reg gen_pca treat_gen_bi if female==0, robust
estimates store Men 

reg gen_pca treat_gen_bi if female==1, robust
estimates store Women

coefplot Men Women, title("(C) DV: Sexist attitudes (PCA)") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons) ylabel("") xtitle("Standardised coefficients")  levels(95 90) ciopts(recast(. rcap))
graph save panelC, replace

****** PANEL D, pca with controls ****** 

reg gen_pca treat_gen_bi age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email if female==0, robust
estimates store Men

reg gen_pca treat_gen_bi age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email if female==1, robust
estimates store Women

coefplot Men Women, title("(D) DV: Sexist attitudes (PCA) with controls") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email) ylabel("") xtitle("Standardised coefficients")  levels(95 90) ciopts(recast(. rcap))
graph save panelD, replace

graph combine "panelA" "panelB" "panelC" "panelD", title("") cols(2) 
graph save sexism, replace




****************************************************************
******* Figure 4: Treatment impact on Club's social role *******
****************************************************************
  
****** PANEL A ******

reg role_race treat_gen_bi if female==0, robust
estimates store Men

reg role_race treat_gen_bi if female==1, robust
estimates store Women

coefplot Men Women, title("(A) DV: Racial Equality") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons) ylabel("") xtitle("Standardised coefficients") levels(95 90) ciopts(recast(. rcap))
graph save panelA, replace


****** PANEL B ******

reg role_gender treat_gen_bi if female==0, robust
estimates store Men

reg role_gender treat_gen_bi if female==1, robust
estimates store Women

coefplot Men Women, title("(B) DV: Gender Equality") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons) ylabel("") xtitle("Standardised coefficients") levels(95 90) ciopts(recast(. rcap))
graph save panelB, replace

****** PANEL C ******

reg role_harrassment treat_gen_bi if female==0, robust
estimates store Men

reg role_harrassment treat_gen_bi if female==1, robust
estimates store Women

coefplot Men Women, title("(C) DV: Sexual Harassment") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons) ylabel("") xtitle("Standardised coefficients") levels(95 90) ciopts(recast(. rcap))
graph save panelC, replace

****** PANEL D ******

reg role_lgbt treat_gen_bi if female==0, robust
estimates store Men

reg role_lgbt treat_gen_bi if female==1, robust
estimates store Women

coefplot Men Women, title("(D) DV: LGBT-phobia") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons) ylabel("") xtitle("Standardised coefficients") levels(95 90) ciopts(recast(. rcap))
graph save panelD, replace

****** PANEL E ******

reg role_inclusion treat_gen_bi if female==0, robust
estimates store Men

reg role_inclusion treat_gen_bi if female==1, robust
estimates store Women

coefplot Men Women, title("(E) DV: Social Inclusion") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons) ylabel("") xtitle("Standardised coefficients") levels(95 90) ciopts(recast(. rcap))
graph save panelE, replace

****** PANEL F ******

reg role_environment treat_gen_bi if female==0, robust
estimates store Men

reg role_environment treat_gen_bi if female==1, robust
estimates store Women

coefplot Men Women, title("(F) DV: Environmental Protection") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons) ylabel("") xtitle("Standardised coefficients") levels(95 90) ciopts(recast(. rcap))
graph save panelF, replace

graph combine "panelA" "panelB" "panelC" "panelD" "panelE" "panelF" , title("") cols(2) 
graph save socialrole, replace




*******************************************************************************************
******* Figure 5: Treatment impact on campaign evaluation and Club's responsiveness *******
*******************************************************************************************

****** PANEL A, campaign evaluation (index) ****** 

reg campaign_both treat_gen_bi if female==0, robust
estimates store Men

reg campaign_both treat_gen_bi if female==1, robust
estimates store Women


coefplot Men Women, title("(A) DV: Campaign evaluation (index)") xlabel(-1(.2)1) xline(0, lcolor(red)) drop(_cons) ylabel("") xtitle("Standardised coefficients")  levels(95 90) ciopts(recast(. rcap))
graph save panelA, replace


****** PANEL B, campaign evaluation (index)  with controls ****** 


reg campaign_both treat_gen_bi age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email if female==0, robust
estimates store Men

reg campaign_both treat_gen_bi age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email if female==1, robust
estimates store Women

coefplot Men Women, title("(B) DV: Campaign evaluation (index) with controls") xlabel(-1(.2)1) xline(0, lcolor(red)) drop(_cons age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email) ylabel("") xtitle("Standardised coefficients")  levels(95 90) ciopts(recast(. rcap))
graph save panelB, replace


****** PANEL C, index ****** 

reg club_all treat_gen_bi if female==0, robust
estimates store Men

reg club_all treat_gen_bi if female==1, robust
estimates store Women


coefplot Men Women, title("(C) DV: Club's responsiveness (index)") xlabel(-1(.2)1) xline(0, lcolor(red)) drop(_cons) ylabel("") xtitle("Standardised coefficients")  levels(95 90) ciopts(recast(. rcap))
graph save panelC, replace


****** PANEL D, index with controls ****** 


reg club_all treat_gen_bi age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email if female==0, robust
estimates store Men

reg club_all treat_gen_bi age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email if female==1, robust
estimates store Women


coefplot Men Women, title("(D) DV: Club's responsiveness (index) with controls") xlabel(-1(.2)1) xline(0, lcolor(red)) drop(_cons age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email) ylabel("") xtitle("Standardised coefficients")  levels(95 90) ciopts(recast(. rcap))
graph save panelD, replace


****** PANEL E, pca ****** 

reg club_pca treat_gen_bi if female==0, robust
estimates store Men 

reg club_pca treat_gen_bi if female==1, robust
estimates store Women

coefplot Men Women, title("(E) DV: Club's responsiveness (PCA)") xlabel(-1(.2)1) xline(0, lcolor(red)) drop(_cons) ylabel("") xtitle("Standardised coefficients")  levels(95 90) ciopts(recast(. rcap))
graph save panelE, replace


****** PANEL F, pca with controls ****** 

reg club_pca treat_gen_bi age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email if female==0, robust
estimates store Men

reg club_pca treat_gen_bi age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email if female==1, robust
estimates store Women

coefplot Men Women, title("(F) DV: Club's responsiveness (PCA) with controls") xlabel(-1(.2)1) xline(0, lcolor(red)) drop(_cons age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email) ylabel("") xtitle("Standardised coefficients")  levels(95 90) ciopts(recast(. rcap))
graph save panelF, replace


graph combine "panelA" "panelB" "panelC" "panelD" "panelE" "panelF", title("") cols(2) 
graph save responsiveness, replace







*************************************************************
******** COMPLEMENTARY ANALYSES REPORTED IN APPENDIX ********
*************************************************************

*****************************************************************************
******* Appendix A: Football teams as non-value oriented organisation *******
*****************************************************************************

*** Note: Analysis conducted with data from national survey with Brazilian respondents and detailed in separate do-file. 


*******************************************
******* Appendix B: Research ethics *******
*******************************************

*** Note: See appendix 


**********************************************************
******* Appendix C: Text and images used in primes *******
**********************************************************

*** Note: See appendix 


*********************************************************
******* Appendix D: Outcome and control variables *******
*********************************************************

*** Note: If running the code in one go, outcome variables will be standardised. In the below tables, we report original (i.e., uncentred) values.  


*** Outcome variables 


sum gen_all gen_pca gen_1 gen_2 gen_3 gen_4 gen_5 gen_6 /// 
role_race role_gender role_harrassment role_lgbt role_inclusion role_environment ///
club_all club_pca

sum gen_all gen_pca gen_1 gen_2 gen_3 gen_4 gen_5 gen_6 /// 
role_race role_gender role_harrassment role_lgbt role_inclusion role_environment ///
club_all club_pca if treat_gen_bi==0

sum gen_all gen_pca gen_1 gen_2 gen_3 gen_4 gen_5 gen_6 /// 
role_race role_gender role_harrassment role_lgbt role_inclusion role_environment ///
club_all club_pca if treat_gen_bi==1



*** Control variables 

sum female age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email



*****************************************
******* Appendix E: Balance tests *******
*****************************************

*** Figure *** 

reg female i.treat_gen_bi 
estimates store Woman

reg age i.treat_gen_bi 
estimates store Age

reg white i.treat_gen_bi
estimates store White 

reg educ i.treat_gen_bi 
estimates store Education

reg catol i.treat_gen_bi 
estimates store Catholic

reg income i.treat_gen_bi 
estimates store Income

reg pbf i.treat_gen_bi 
estimates store Cash_transfer

reg marital i.treat_gen_bi
estimates store Marital

reg socio i.treat_gen_bi
estimates store Club_member

reg torcida i.treat_gen_bi
estimates store Torcida 

reg satsfaction_director i.treat_gen_bi
estimates store Sat_Adm

reg freq_stadium_bi i.treat_gen_bi
estimates store Stadium_attendance_bi 

reg coleta_email i.treat_gen_bi
estimates store Recruitment_type


coefplot Woman Age White Education Catholic Income Cash_transfer Marital Club_member Torcida Sat_Adm Stadium_attendance_bi Recruitment_type, title("") xline(0, lcolor(red)) drop(_cons) xlabel(-.5(.1).5) ylabel("") xtitle("Coefficient") levels(95) ciopts(recast(. rcap))
graph save balance, replace


*** Table ***

eststo:reg female i.treat_gen_bi 
eststo:reg age i.treat_gen_bi 
eststo:reg white i.treat_gen_bi
eststo:reg educ i.treat_gen_bi 
eststo:reg catol i.treat_gen_bi 
eststo:reg income i.treat_gen_bi 
eststo:reg pbf i.treat_gen_bi 
eststo:reg marital i.treat_gen_bi
eststo:reg socio i.treat_gen_bi
eststo:reg torcida i.treat_gen_bi
eststo:reg satsfaction_director i.treat_gen_bi
eststo:reg freq_stadium_bi i.treat_gen_bi
eststo:reg coleta_email i.treat_gen_bi

esttab using balance.tex, label replace booktabs ///
alignment(D{.}{.}{-1})                         ///
star(* 0.10 ** 0.05 *** 0.01) se ///
collabels(none) varlabels(_cons "Constant") ///
title(Balance tests\label{tab1})
eststo clear


*****************************************************************************
******* Appendix F: Average treatment effects (ATE): Sexist attitudes *******
*****************************************************************************

*** Note: If you reloaded the data to run the above descriptive statistics in original values, make sure to recenter outcome variables so results are shown in standard deviations. 


*** Figure 

****** PANEL A, index ****** 

reg gen_all treat_gen_bi, robust
estimates store sexist 
coefplot sexist, title("(A) DV: Sexist attitudes (index)") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons) ylabel("") xtitle("Standardised coefficients") levels(95 90) ciopts(recast(. rcap))
graph save panelA, replace

****** PANEL B, index with controls ****** 

reg gen_all treat_gen_bi female age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email, robust
estimates store sexist_controls

coefplot sexist_controls, title("(B) DV: Sexist attitudes (index) with controls") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons female age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email) ylabel("") xtitle("Standardised coefficients") levels(95 90) ciopts(recast(. rcap))
graph save panelB, replace

****** PANEL C, pca ****** 

reg gen_pca treat_gen_bi, robust
estimates store gen_pca 

coefplot gen_pca, title("(C) DV: Sexist attitudes (PCA)") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons) ylabel("") xtitle("Standardised coefficients")  levels(95 90) ciopts(recast(. rcap))
graph save panelC, replace

****** PANEL D, pca with controls ****** 

reg gen_pca treat_gen_bi female age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email, robust
estimates store gen_pcacontrols

coefplot gen_pcacontrols, title("(D) DV: Sexist attitudes (PCA) with controls") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons female age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email) ylabel("") xtitle("Standardised coefficients")  levels(95 90) ciopts(recast(. rcap))
graph save panelD, replace

graph combine "panelA" "panelB" "panelC" "panelD", title("") cols(2) 
graph save sexism, replace


*** Table

eststo:reg  gen_all i.treat_gen_bi, robust
eststo:reg gen_all i.treat_gen_bi female age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email, robust
eststo:reg gen_pca i.treat_gen_bi, robust
eststo:reg gen_pca i.treat_gen_bi female age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email, robust

esttab using figure3results.tex, label replace booktabs ///
alignment(D{.}{.}{-1})                         ///
star(* 0.10 ** 0.05 *** 0.01) se ///
collabels(none) varlabels(_cons "Constant") ///
title(The effect of ECB's campaign on fans' sexist attitudes\label{tab1})
eststo clear	



*******************************************************************************************
******* Appendix G: Treatment effects on individual items of sexist attitudes index *******
*******************************************************************************************


*** Centering individual items so results are shown in standard deviations 
center gen_1 gen_2 gen_3 gen_4 gen_5 gen_6, inplace standardize 

su gen_1 gen_2 gen_3 gen_4 gen_5 gen_6


*** Figure 
  
****** PANEL A ******

reg gen_1 treat_gen_bi if female==0, robust
estimates store Men

reg gen_1 treat_gen_bi if female==1, robust
estimates store Women

coefplot Men Women, title("(A) DV: Performance") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons) ylabel("") xtitle("Standardised coefficients") levels(95 90) ciopts(recast(. rcap))
graph save panelA, replace


****** PANEL B ******

reg gen_2 treat_gen_bi if female==0, robust
estimates store Men

reg gen_2 treat_gen_bi if female==1, robust
estimates store Women

coefplot Men Women, title("(B) DV: Manager") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons) ylabel("") xtitle("Standardised coefficients") levels(95 90) ciopts(recast(. rcap))
graph save panelB, replace

****** PANEL C ******

reg gen_3 treat_gen_bi if female==0, robust
estimates store Men

reg gen_3 treat_gen_bi if female==1, robust
estimates store Women

coefplot Men Women, title("(C) DV: Salary") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons) ylabel("") xtitle("Standardised coefficients") levels(95 90) ciopts(recast(. rcap))
graph save panelC, replace

****** PANEL D ******

reg gen_4 treat_gen_bi if female==0, robust
estimates store Men

reg gen_4 treat_gen_bi if female==1, robust
estimates store Women

coefplot Men Women, title("(D) DV: Samarter Play") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons) ylabel("") xtitle("Standardised coefficients") levels(95 90) ciopts(recast(. rcap))
graph save panelD, replace

****** PANEL E ******

reg gen_5 treat_gen_bi if female==0, robust
estimates store Men

reg gen_5 treat_gen_bi if female==1, robust
estimates store Women

coefplot Men Women, title("(E) DV: Referees") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons) ylabel("") xtitle("Standardised coefficients") levels(95 90) ciopts(recast(. rcap))
graph save panelE, replace

****** PANEL F ******

reg gen_6 treat_gen_bi if female==0, robust
estimates store Men

reg gen_6 treat_gen_bi if female==1, robust
estimates store Women

coefplot Men Women, title("(F) DV: Men's Football") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons) ylabel("") xtitle("Standardised coefficients") levels(95 90) ciopts(recast(. rcap))
graph save panelF, replace

graph combine "panelA" "panelB" "panelC" "panelD" "panelE" "panelF" , title("") cols(2) 
graph save socialrole, replace



*************************************************
******* Appendix H: PCA: Sexist attitudes *******
*************************************************

pca gen_1 gen_2 gen_3 gen_4 gen_5 gen_6

screeplot, title("Sexist attitudes") xtitle("Number of factors") yline(1, lcolor(red)) ylabel(0(1)4) ci(het) legend(cols(2) position(6)) 
graph save gen_pca, replace 

predict gen_pca, score 



***************************************************************
******* Appendix I: Tables of results: Sexist attitudes *******
***************************************************************


*** Heterogeneous treatment effects - Men respondents 

eststo: reg gen_all treat_gen_bi if female==0, robust
eststo: reg gen_all treat_gen_bi age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email if female==0, robust
eststo: reg gen_pca treat_gen_bi if female==0, robust
eststo: reg gen_pca treat_gen_bi age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email if female==0, robust

esttab using sexism_men.tex, label replace booktabs ///
alignment(D{.}{.}{-1})                         ///
star(* 0.10 ** 0.05 *** 0.01) se ///
collabels(none) varlabels(_cons "Constant") ///
title(The effect of ECB's campaign on fans' sexist attitudes - Men respondents \label{sexism_men})
eststo clear

*** Heterogeneous treatment effects - Women respondents 

eststo: reg gen_all treat_gen_bi if female==1, robust
eststo: reg gen_all treat_gen_bi age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email if female==1, robust
eststo: reg gen_pca treat_gen_bi if female==1, robust
eststo: reg gen_pca treat_gen_bi age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email if female==1, robust

esttab using sexism_women.tex, label replace booktabs ///
alignment(D{.}{.}{-1})                         ///
star(* 0.10 ** 0.05 *** 0.01) se ///
collabels(none) varlabels(_cons "Constant") ///
title(The effect of ECB's campaign on fans' sexist attitudes - Women respondents \label{sexism_women})
eststo clear


*** Average treatment effect

eststo:reg  gen_all i.treat_gen_bi, robust
eststo:reg gen_all i.treat_gen_bi female age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email, robust
eststo:reg gen_pca i.treat_gen_bi, robust
eststo:reg gen_pca i.treat_gen_bi female age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email, robust

esttab using sexism_ate.tex, label replace booktabs ///
alignment(D{.}{.}{-1})                         ///
star(* 0.10 ** 0.05 *** 0.01) se ///
collabels(none) varlabels(_cons "Constant") ///
title(The effect of ECB's campaign on fans' sexist attitudes\label{sexism_ate})
eststo clear	



**************************************************************
******* Appendix J: Profile of Brazilian football fans *******
**************************************************************

*** Note: Analysis conducted with data from national survey with Brazilian respondents and detailed in separate do-file. 



*******************************************************************************
******* Appendix K: Average treatment effects (ATE): Club's social role *******
*******************************************************************************


****** PANEL A ******

reg role_race treat_gen_bi, robust
estimates store race


coefplot race, title("(A) DV: Racial Equality") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons) ylabel("") xtitle("Standardised coefficients") levels(95 90) ciopts(recast(. rcap))
graph save panelA, replace


****** PANEL B ******

reg role_gender treat_gen_bi, robust
estimates store gender


coefplot gender, title("(B) DV: Gender Equality") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons) ylabel("") xtitle("Standardised coefficients") levels(95 90) ciopts(recast(. rcap))
graph save panelB, replace

****** PANEL C ******

reg role_harrassment treat_gen_bi, robust
estimates store hara


coefplot hara, title("(C) DV: Sexual Harassment") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons) ylabel("") xtitle("Standardised coefficients") levels(95 90) ciopts(recast(. rcap))
graph save panelC, replace

****** PANEL D ******

reg role_lgbt treat_gen_bi, robust
estimates store lgbt


coefplot lgbt, title("(D) DV: LGBT-phobia") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons) ylabel("") xtitle("Standardised coefficients") levels(95 90) ciopts(recast(. rcap))
graph save panelD, replace

****** PANEL E ******

reg role_inclusion treat_gen_bi, robust
estimates store incl


coefplot incl, title("(E) DV: Social Inclusion") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons) ylabel("") xtitle("Standardised coefficients") levels(95 90) ciopts(recast(. rcap))
graph save panelE, replace

****** PANEL F ******

reg role_environment treat_gen_bi, robust
estimates store envi

coefplot envi, title("(F) DV: Environmental Protection") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons) ylabel("") xtitle("Standardised coefficients") levels(95 90) ciopts(recast(. rcap))
graph save panelF, replace

graph combine "panelA" "panelB" "panelC" "panelD" "panelE" "panelF" , title("") cols(2) 
graph save socialrole, replace



*****************************************************************
******* Appendix L: Tables of results: Club's social role *******
*****************************************************************


*** Heterogeneous treatment effects - Men respondents 

eststo: reg role_race treat_gen_bi if female==0, robust
eststo: reg role_gender treat_gen_bi if female==0, robust
eststo: reg role_harrassment treat_gen_bi if female==0, robust
eststo: reg role_lgbt treat_gen_bi if female==0, robust
eststo: reg role_inclusion treat_gen_bi if female==0, robust
eststo: reg role_environment treat_gen_bi if female==0, robust

esttab using socialrole_men.tex, label replace booktabs ///
alignment(D{.}{.}{-1})                         ///
star(* 0.10 ** 0.05 *** 0.01) se ///
collabels(none) varlabels(_cons "Constant") ///
title(The effect of ECB's campaign on fans' sexist attitudes - Men respondents \label{socialrole_men})
eststo clear

*** Heterogeneous treatment effects - Women respondents 

eststo: reg role_race treat_gen_bi if female==1, robust
eststo: reg role_gender treat_gen_bi if female==1, robust
eststo: reg role_harrassment treat_gen_bi if female==1, robust
eststo: reg role_lgbt treat_gen_bi if female==1, robust
eststo: reg role_inclusion treat_gen_bi if female==1, robust
eststo: reg role_environment treat_gen_bi if female==1, robust

esttab using socialrole_women.tex, label replace booktabs ///
alignment(D{.}{.}{-1})                         ///
star(* 0.10 ** 0.05 *** 0.01) se ///
collabels(none) varlabels(_cons "Constant") ///
title(The effect of ECB's campaign on fans' sexist attitudes - Women respondents \label{socialrole_women})
eststo clear

***  Average treatment effect 

eststo:reg role_race treat_gen_bi, robust
eststo:reg role_gender treat_gen_bi, robust
eststo:reg role_harrassment treat_gen_bi, robust
eststo:reg role_lgbt treat_gen_bi, robust
eststo:reg role_inclusion treat_gen_bi, robust
eststo:reg role_environment treat_gen_bi, robust 

esttab using socialrole_ate.tex, label replace booktabs ///
alignment(D{.}{.}{-1})                         ///
star(* 0.10 ** 0.05 *** 0.01) se ///
collabels(none) varlabels(_cons "Constant") ///
title(The effect of ECB's campaign on fans' attitudes towards the Club's social role\label{socialrole_ate})
eststo clear


*********************************************************************************************************
******* Appendix M: Average treatment effects (ATE): Campaign evaluations and Club responsiveness *******
*********************************************************************************************************


****** PANEL A, campaign evaluation (index) ****** 

reg campaign_both treat_gen_bi, robust
estimates store campaign

coefplot campaign, title("(A) DV: Campaign evaluation (index)") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons) ylabel("") xtitle("Standardised coefficients")  levels(95 90) ciopts(recast(. rcap))
graph save panelA, replace


****** PANEL B, campaign evaluation (index)  with controls ****** 


reg campaign_both treat_gen_bi age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email, robust
estimates store campaign_con


coefplot campaign_con, title("(B) DV: Campaign evaluation (index) with controls") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email) ylabel("") xtitle("Standardised coefficients")  levels(95 90) ciopts(recast(. rcap))
graph save panelB, replace


****** PANEL C, index ****** 

reg club_all treat_gen_bi, robust
estimates store club


coefplot club, title("(C) DV: Club's responsiveness (index)") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons) ylabel("") xtitle("Standardised coefficients")  levels(95 90) ciopts(recast(. rcap))
graph save panelC, replace


****** PANEL D, index with controls ****** 


reg club_all treat_gen_bi age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email, robust
estimates store club_con

coefplot club_con, title("(D) DV: Club's responsiveness (index) with controls") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email) ylabel("") xtitle("Standardised coefficients")  levels(95 90) ciopts(recast(. rcap))
graph save panelD, replace


****** PANEL E, pca ****** 

reg club_pca treat_gen_bi, robust
estimates store clubpca 

coefplot clubpca , title("(E) DV: Club's responsiveness (PCA)") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons) ylabel("") xtitle("Standardised coefficients")  levels(95 90) ciopts(recast(. rcap))
graph save panelE, replace


****** PANEL F, pca with controls ****** 

reg club_pca treat_gen_bi age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email, robust
estimates store clubpca_con

coefplot clubpca_con, title("(F) DV: Club's responsiveness (PCA) with controls") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email) ylabel("") xtitle("Standardised coefficients")  levels(95 90) ciopts(recast(. rcap))
graph save panelF, replace


graph combine "panelA" "panelB" "panelC" "panelD" "panelE" "panelF", title("") cols(2) 
graph save responsiveness, replace




*******************************************************************************************
******* Appendix N: Tables of results: Campaign evaluations and club responsiveness *******
*******************************************************************************************

**************************
** Campaign evaluations ** 
**************************

*** Heterogeneous treatment effects - Men respondents 

eststo:reg campaign_both treat_gen_bi if female==0, robust
eststo:reg campaign_both treat_gen_bi age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email if female==0, robust

esttab using camp_men.tex, label replace booktabs ///
alignment(D{.}{.}{-1})                         ///
star(* 0.10 ** 0.05 *** 0.01) se ///
collabels(none) varlabels(_cons "Constant") ///
title(The effect of ECB's campaign on fans' campaign evaluations - Men respondents \label{camp_men})
eststo clear

*** Heterogeneous treatment effects - Women respondents 

eststo:reg campaign_both treat_gen_bi if female==1, robust
eststo:reg campaign_both treat_gen_bi age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email if female==1, robust

esttab using camp_women.tex, label replace booktabs ///
alignment(D{.}{.}{-1})                         ///
star(* 0.10 ** 0.05 *** 0.01) se ///
collabels(none) varlabels(_cons "Constant") ///
title(The effect of ECB's campaign on fans' campaign evaluations - Women respondents \label{camp_women})
eststo clear

***  Average treatment effect 

eststo:reg campaign_both treat_gen_bi, robust
eststo:reg campaign_both treat_gen_bi age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email, robust


esttab using camp_ate.tex, label replace booktabs ///
alignment(D{.}{.}{-1})                         ///
star(* 0.10 ** 0.05 *** 0.01) se ///
collabels(none) varlabels(_cons "Constant") ///
title(The effect of ECB's campaign on fans' campaign evaluations - All respondents\label{camp_ate})
eststo clear

*************************
** Club responsiveness ** 
*************************


*** Heterogeneous treatment effects - Men respondents 

eststo:reg club_all treat_gen_bi if female==0, robust
eststo:reg club_all treat_gen_bi age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email if female==0, robust
eststo:reg club_pca treat_gen_bi if female==0, robust
eststo:reg club_pca treat_gen_bi age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email if female==0, robust

esttab using resp_men.tex, label replace booktabs ///
alignment(D{.}{.}{-1})                         ///
star(* 0.10 ** 0.05 *** 0.01) se ///
collabels(none) varlabels(_cons "Constant") ///
title(The effect of ECB's campaign on fans' perceptions of Club's responsivess - Men respondents \label{resp_men})
eststo clear

*** Heterogeneous treatment effects - Women respondents 

eststo:reg club_all treat_gen_bi if female==1, robust
eststo:reg club_all treat_gen_bi age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email if female==0, robust
eststo:reg club_pca treat_gen_bi if female==1, robust
eststo:reg club_pca treat_gen_bi age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email if female==1, robust

esttab using resp_women.tex, label replace booktabs ///
alignment(D{.}{.}{-1})                         ///
star(* 0.10 ** 0.05 *** 0.01) se ///
collabels(none) varlabels(_cons "Constant") ///
title(The effect of ECB's campaign on fans' perceptions of Club's responsivess - Women respondents \label{resp_women})
eststo clear

***  Average treatment effect 

eststo:reg club_all treat_gen_bi, robust
eststo:reg club_all treat_gen_bi age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email, robust
eststo:reg club_pca treat_gen_bi, robust
eststo:reg club_pca treat_gen_bi age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email, robust

esttab using resp_ate.tex, label replace booktabs ///
alignment(D{.}{.}{-1})                         ///
star(* 0.10 ** 0.05 *** 0.01) se ///
collabels(none) varlabels(_cons "Constant") ///
title(The effect of ECB's campaign on fans' perceptions of Club's responsivess - All respondents\label{resp_ate})
eststo clear


****************************************************
******* Appendix O: PCA: Club responsiveness *******
****************************************************

pca club_1 club_2 club_3

screeplot, title("Club responsiveness") xtitle("Number of factors") yline(1, lcolor(red)) ylabel(0(1)4) ci(het) legend(cols(2) position(6)) 
graph save gen_pca, replace 

predict club_pca, score 


***********************************************************************
******* Appendix P: Analyses of exploratory treatment condition *******
***********************************************************************


*** Balance tests *** 

reg female i.treat_gen
estimates store Woman

reg age i.treat_gen
estimates store Age

reg white i.treat_gen
estimates store White 

reg educ i.treat_gen 
estimates store Education

reg catol i.treat_gen
estimates store Catholic

reg income i.treat_gen
estimates store Income

reg pbf i.treat_gen
estimates store Cash_transfer

reg marital i.treat_gen
estimates store Marital

reg socio i.treat_gen
estimates store Club_member

reg torcida i.treat_gen
estimates store Torcida 

reg satsfaction_director i.treat_gen
estimates store Sat_Adm

reg freq_stadium_bi i.treat_gen
estimates store Stadium_attendance_bi 

reg coleta_email i.treat_gen
estimates store Recruitment_type


coefplot Woman Age White Education Catholic Income Cash_transfer Marital Club_member Torcida Sat_Adm Stadium_attendance_bi Recruitment_type, title("") xline(0, lcolor(red)) drop(_cons) xlabel(-.5(.1).5) ylabel("") xtitle("Coefficient") levels(95) ciopts(recast(. rcap))
graph save balance, replace


*** Treatment impact on sexist attitudes ***


* PANEL A, index

reg gen_all i.treat_gen if female==0, robust
estimates store Men

reg gen_all i.treat_gen if female==1, robust
estimates store Women

coefplot Men Women, title("(A) DV: Sexist attitudes (index)") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons) ylabel(1 "Exploratory Treatment" 2 "Main Treatment") xtitle("Standardised coefficients") levels(95 90) ciopts(recast(. rcap))
graph save panelA, replace

* PANEL B, index with controls

reg gen_all i.treat_gen female age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email if female==0, robust
estimates store Men

reg gen_all i.treat_gen female age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email if female==1, robust
estimates store Women

coefplot Men Women, title("(B) DV: Sexist attitudes (index) with controls") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons female age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email) ylabel(1 "Exploratory Treatment" 2 "Main Treatment") xtitle("Standardised coefficients") levels(95 90) ciopts(recast(. rcap))
graph save panelB, replace

* PANEL C, pca 

reg gen_pca i.treat_gen if female==0, robust
estimates store Men

reg gen_pca i.treat_gen if female==1, robust
estimates store Women

coefplot Men Women, title("(C) DV: Sexist attitudes (PCA)") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons) ylabel(1 "Exploratory Treatment" 2 "Main Treatment") xtitle("Standardised coefficients")  levels(95 90) ciopts(recast(. rcap))
graph save panelC, replace

* PANEL D, pca with controls 

reg gen_pca i.treat_gen female age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email if female==0, robust
estimates store Men

reg gen_pca i.treat_gen female age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email if female==1, robust
estimates store Women

coefplot Men Women, title("(D) DV: Sexist attitudes (PCA) with controls") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons female age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email) ylabel(1 "Exploratory Treatment" 2 "Main Treatment") xtitle("Standardised coefficients")  levels(95 90) ciopts(recast(. rcap))
graph save panelD, replace

graph combine "panelA" "panelB" "panelC" "panelD", title("") cols(2) 
graph save sexism, replace



*** Treatment impact on Club's social role ***


****** PANEL A ******

reg role_race i.treat_gen if female==0, robust
estimates store Men

reg role_race i.treat_gen if female==1, robust
estimates store Women

coefplot Men Women, title("(A) DV: Racial Equality") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons) ylabel(1 "Exploratory Treatment" 2 "Main Treatment") xtitle("Standardised coefficients") levels(95 90) ciopts(recast(. rcap))
graph save panelA, replace


****** PANEL B ******

reg role_gender i.treat_gen if female==0, robust
estimates store Men

reg role_gender i.treat_gen if female==1, robust
estimates store Women

coefplot Men Women, title("(B) DV: Gender Equality") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons) ylabel(1 "Exploratory Treatment" 2 "Main Treatment") xtitle("Standardised coefficients") levels(95 90) ciopts(recast(. rcap))
graph save panelB, replace

****** PANEL C ******

reg role_harrassment i.treat_gen if female==0, robust
estimates store Men

reg role_harrassment i.treat_gen if female==1, robust
estimates store Women

coefplot Men Women, title("(C) DV: Sexual Harassment") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons) ylabel(1 "Exploratory Treatment" 2 "Main Treatment") xtitle("Standardised coefficients") levels(95 90) ciopts(recast(. rcap))
graph save panelC, replace

****** PANEL D ******

reg role_lgbt i.treat_gen if female==0, robust
estimates store Men

reg role_lgbt i.treat_gen if female==1, robust
estimates store Women

coefplot Men Women, title("(D) DV: LGBT-phobia") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons) ylabel(1 "Exploratory Treatment" 2 "Main Treatment") xtitle("Standardised coefficients") levels(95 90) ciopts(recast(. rcap))
graph save panelD, replace

****** PANEL E ******

reg role_inclusion i.treat_gen if female==0, robust
estimates store Men

reg role_inclusion i.treat_gen if female==1, robust
estimates store Women

coefplot Men Women, title("(E) DV: Social Inclusion") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons) ylabel(1 "Exploratory Treatment" 2 "Main Treatment") xtitle("Standardised coefficients") levels(95 90) ciopts(recast(. rcap))
graph save panelE, replace

****** PANEL F ******

reg role_environment i.treat_gen if female==0, robust
estimates store Men

reg role_environment i.treat_gen if female==1, robust
estimates store Women

coefplot Men Women, title("(F) DV: Environmental Protection") xlabel(-.5(.1).5) xline(0, lcolor(red)) drop(_cons) ylabel(1 "Exploratory Treatment" 2 "Main Treatment") xtitle("Standardised coefficients") levels(95 90) ciopts(recast(. rcap))
graph save panelF, replace

graph combine "panelA" "panelB" "panelC" "panelD" "panelE" "panelF" , title("") cols(2) 
graph save socialrole, replace



*** Treatment impact on campaign evaluations and club responsiveness ***


****** PANEL A, campaign evaluation (index) ****** 

reg campaign_both i.treat_gen if female==0, robust
estimates store Men

reg campaign_both i.treat_gen if female==1, robust
estimates store Women


coefplot Men Women, title("(A) DV: Campaign evaluation (index)") xlabel(-1(.2)1) xline(0, lcolor(red)) drop(_cons) ylabel(1 "Exploratory Treatment" 2 "Main Treatment") xtitle("Standardised coefficients")  levels(95 90) ciopts(recast(. rcap))
graph save panelA, replace


****** PANEL B, campaign evaluation (index)  with controls ****** 


reg campaign_both i.treat_gen age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email if female==0, robust
estimates store Men

reg campaign_both i.treat_gen age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email if female==1, robust
estimates store Women

coefplot Men Women, title("(B) DV: Campaign evaluation (index) with controls") xlabel(-1(.2)1) xline(0, lcolor(red)) drop(_cons age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email) ylabel(1 "Exploratory Treatment" 2 "Main Treatment") xtitle("Standardised coefficients")  levels(95 90) ciopts(recast(. rcap))
graph save panelB, replace


****** PANEL C, index ****** 

reg club_all i.treat_gen if female==0, robust
estimates store Men

reg club_all i.treat_gen if female==1, robust
estimates store Women


coefplot Men Women, title("(C) DV: Club's responsiveness (index)") xlabel(-1(.2)1) xline(0, lcolor(red)) drop(_cons) ylabel(1 "Exploratory Treatment" 2 "Main Treatment") xtitle("Standardised coefficients")  levels(95 90) ciopts(recast(. rcap))
graph save panelC, replace


****** PANEL D, index with controls ****** 


reg club_all i.treat_gen age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email if female==0, robust
estimates store Men

reg club_all i.treat_gen age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email if female==1, robust
estimates store Women


coefplot Men Women, title("(D) DV: Club's responsiveness (index) with controls") xlabel(-1(.2)1) xline(0, lcolor(red)) drop(_cons age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email) ylabel(1 "Exploratory Treatment" 2 "Main Treatment") xtitle("Standardised coefficients")  levels(95 90) ciopts(recast(. rcap))
graph save panelD, replace


****** PANEL E, pca ****** 

reg club_pca i.treat_gen if female==0, robust
estimates store Men 

reg club_pca i.treat_gen if female==1, robust
estimates store Women

coefplot Men Women, title("(E) DV: Club's responsiveness (PCA)") xlabel(-1(.2)1) xline(0, lcolor(red)) drop(_cons) ylabel(1 "Exploratory Treatment" 2 "Main Treatment") xtitle("Standardised coefficients")  levels(95 90) ciopts(recast(. rcap))
graph save panelE, replace


****** PANEL F, pca with controls ****** 

reg club_pca i.treat_gen age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email if female==0, robust
estimates store Men

reg club_pca i.treat_gen age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email if female==1, robust
estimates store Women

coefplot Men Women, title("(F) DV: Club's responsiveness (PCA) with controls") xlabel(-1(.2)1) xline(0, lcolor(red)) drop(_cons age white educ catol income pbf marital socio torcida satsfaction_director freq_stadium_bi coleta_email) ylabel(1 "Exploratory Treatment" 2 "Main Treatment") xtitle("Standardised coefficients")  levels(95 90) ciopts(recast(. rcap))
graph save panelF, replace


graph combine "panelA" "panelB" "panelC" "panelD" "panelE" "panelF", title("") cols(2) 
graph save responsiveness, replace


